Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines. (15th March 2020)

• Record Type: Journal Article
• Authors: Chen, Huiling; Li, Shimin; Asghar Heidari, Ali; Wang, Pengjun; Li, Jiawei; Yang, Yutao; Wang, Mingjing; Huang, Changcheng
• Is Part Of: Expert systems with applications. Volume 142(2020)
• Publication Date: 2020-03-15
• Subjects: Fruit fly optimization algorithm -- Parameter optimization -- Outpost mechanism -- Multi-population mechanism -- Support vector machine; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.112999
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Outpost mechanism and multi-swarm mechanism are introduced into FOA. The enhanced FOA is used for constructing an evolutionary support vector machine. Classic and CEC benchmark problems are used to verify the proposed method. The proposed method is also applied for predicting the financial stress problems. Abstract: The original fruit fly algorithm (FOA) in simple structure is easy to understand, but it has a slow convergence rate and tends to be trapped in the local optimal solutions. In order to improve the convergence rate and efficacy of FOA, two new mechanisms are integrated with the exploratory and exploitative strategies of the original FOA: the outpost mechanism and the multi-population mechanism. The outpost mechanism consists of two parts: greedy selection and Gaussian mutation, which is mainly used to improve the convergence rate of the algorithm. The multi-swarm mechanism divides the population of agents into several sub-swarms and selects several individuals from sub-swarm with a random probability. Then, the selected individuals are remapped into the feature space to expand the exploratory capabilities. To illustrate the performance of the proposed method, a comprehensive set of benchmark functions, including the unimodal, multimodal, and composition functions were chosen for testing tasks. Also, the proposed MOFOA is compared against the state-of-the-art improved FOA algorithms and other well-known swarm-based methods. The experimental results have shown that MOFOA can outperform all the competitors involved in this study in terms of convergence speed and solution quality in a significant manner. Furthermore, MOFOA is also employed to optimize two critical parameters of the support vector machine (SVM) for classification tasks. The results demonstrate that the proposed MOFOA can also achieve a better performance than other swarm-based methods in dealing with the optimization of the SVM in dealing with several financial datasets.